In this section, we introduce HONAM, a novel interpretable machine-learning method that can model arbitrary orders of feature interactions.
First, we describe NAM [1]} and transform it to model feature interactions.
We then present HONAM with element-wise feature crossing.
Finally, we propose a novel feature interaction method for high interpretability and apply it to HONAM.
